关键词: 运动功能评估; 卒中; 多关节解码; 惯性测量单元

Abstract: Objective  This study aims to develop and construct an upper limb multi-joint motion state recognition system based on low-cost inertial measurement unit (IMU) signals, for the rapid and reliable decoding of multi-joint (forearm, elbow joint, and shoulder joint) motion states in human activities, providing support for motion pattern recognition and daily motion monitoring in post-stroke upper limb rehabilitation assessment.
Methods  This study enrolled four healthy subjects, collecting their six-dimensional (triaxial acceleration+triaxial angular velocity) motion signals through IMUs deployed on the wrist and upper arm, each subject repeating 10 times. Based on the flexor synergy movement in the Fugl-Meyer motor assessment-upper extremity, eight subtasks were designed, each corresponding to a ternary state label of the forearm (supinated or not), elbow joint (flexed or not), and shoulder joint (elevated or not). A multi-label classification framework based on single-task migration (i.e., independently training single-joint classifiers and then merging the outputs) was constructed. At the algorithm level, traditional machine learning methods (time-frequency domain features+random forest) were compared with deep learning algorithms (long short-term memory-based end-to-end learning). Five-fold cross-validation was used to evaluate the accuracy of the upper limb multi-joint motion state recognition system, and ablation experiments were designed to analyze the impact of sensor configuration (e.g., wrist-only vs. wrist+arm) on decoding performance, exploring hardware optimization potential.
Results  A total of 320 motion data samples were collected from four healthy subjects in this study. The results demonstrated that the motion state recognition system designed in this study performed well in multi-joint state decoding of the upper limb. The average accuracy of elbow joint state classification by the traditional machine learning methods was 79.37%, while the deep learning model IBNet reached 87.5%, indicating a stronger pattern-learning capability. The ablation experiment showed that the accuracy of elbow joint state classification exceeded that of dual IMU configuration (92.5% vs. 87.5%) when wrist IMU was used only, and the difference was not significant in other tasks. This suggested that optimizing sensor deployment (e.g., reducing upper arm IMUs) can reduce system complexity while maintaining high performance.
Conclusions  This study successfully constructed a low-cost IMU-based upper limb motion state recognition system. The results showed that deep learning algorithms were superior to traditional machine learning in decoding complex motion patterns, and a single-wrist IMU could replace the dual-sensor configuration in specific tasks, providing a basis for hardware optimization.

Key words: Motor function assessment; Stroke; Multi-joint decoding; Inertial measurement unit